HybridBackend

HybridBackend is a high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster.

Features

• Memory-efficient loading of categorical data
• GPU-efficient orchestration of embedding layers
• Communication-efficient training and evaluation at scale
• Easy to use with existing AI workflows

Usage

A minimal example:

```python import tensorflow as tf import hybridbackend.tensorflow as hb

ds = hb.data.Dataset.fromparquet(filenames) ds = ds.batch(batchsize)

...

with tf.device('/gpu:0'): embs = tf.nn.embeddinglookupsparse(weights, input_ids) # ... ```

Please see documentation for more information.

Install

Method 1: Install from PyPI

pip install {PACKAGE}

| {PACKAGE} | Dependency | Python | CUDA | GLIBC | Data Opt. | Embedding Opt. | Parallelism Opt. | | ----------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- | ------ | ---- | ------ | --------- | -------------- | ---------------- | | hybridbackend-tf115-cu121 | TensorFlow 1.15 | 3.8 | 12.1 | >=2.31 | ✓ | ✓ | ✓ | | hybridbackend-tf115-cu100 | TensorFlow 1.15 | 3.6 | 10.0 | >=2.27 | ✓ | ✓ | ✗ | | hybridbackend-tf115-cpu | TensorFlow 1.15 | 3.6 | - | >=2.24 | ✓ | ✗ | ✗ |

Method 2: Build from source

See Building Instructions.

We also provide built docker images for latest DeepRec: registry.cn-shanghai.aliyuncs.com/pai-dlc/hybridbackend:1.0.0-deeprec-py3.6-cu114-ubuntu18.04

License

HybridBackend is licensed under the Apache 2.0 License.

Community

• Please see Contributing Guide before your first contribution.
• Please register as an adopter if your organization is interested in adoption. We will discuss RoadMap with registered adopters in advance.
• Please cite HybridBackend in your publications if it helps:

text @inproceedings{zhang2022picasso, title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems}, author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others}, booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)}, year={2022}, organization={IEEE} }

Contact Us

If you would like to share your experiences with others, you are welcome to contact us in DingTalk: